{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. _nb_gde3:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GDE3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from jmetal.algorithm.multiobjective.gde3 import GDE3\n",
    "from jmetal.problem import ZDT1\n",
    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
    "\n",
    "problem = ZDT1()\n",
    "\n",
    "max_evaluations = 25000\n",
    "\n",
    "algorithm = GDE3(\n",
    "    problem=problem,\n",
    "    population_size=100,\n",
    "    cr=0.5,\n",
    "    f=0.5,\n",
    "    termination_criterion=StoppingByEvaluations(max_evaluations)\n",
    ")\n",
    "\n",
    "algorithm.run()\n",
    "solutions = algorithm.get_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now visualize the Pareto front approximation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from jmetal.lab.visualization.plotting import Plot\n",
    "from jmetal.util.solution import get_non_dominated_solutions\n",
    "\n",
    "front = get_non_dominated_solutions(solutions)\n",
    "    \n",
    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
    "plot_front.plot(front, label='GDE3-ZDT1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### API"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%% raw\n"
    },
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. autoclass:: jmetal.algorithm.multiobjective.gde3.GDE3\n",
    "   :members:\n",
    "   :undoc-members:\n",
    "   :show-inheritance:\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
